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About This Role
Company Description
At EVERSANA, we are proud to be certified as a Great Place to Work across the globe. We’re fueled by our vision to create a healthier world. How? Our global team of more than 7,000 employees is committed to creating and delivering next\-generation commercialization services to the life sciences industry. We are grounded in our cultural beliefs and serve more than 650 clients ranging from innovative biotech start\-ups to established pharmaceutical companies. Our products, services and solutions help bring innovative therapies to market and support the patients who depend on them. Our jobs, skills and talents are unique, but together we make an impact every day. Join us!
Across our growing organization, we embrace diversity in backgrounds and experiences. Improving patient lives around the world is a priority, and we need people from all backgrounds and swaths of life to help build the future of the healthcare and the life sciences industry. We believe our people make all the difference in cultivating an inclusive culture that embraces our cultural beliefs. We are deliberate and self\-reflective about the kind of team and culture we are building. We look for team members that are not only strong in their own aptitudes but also who care deeply about EVERSANA, our people, clients and most importantly, the patients we serve. We are EVERSANA.
Job Description THE POSITION:
EVERSANA is standing up an AI Hub Center of Excellence within Patient Services Technology to transform how we build and deliver software. The AI Engineering \& Enablement Lead owns this mission: to ingest enterprise AI tooling and productionize it into the Patient Services SDLC, build a governed framework for deploying and maintaining AI agents, establish engineering best practices, and re\-architect our current engineering practice into an AI\-augmented software development organization.
This is a player\-coach role. The Lead is the onshore anchor of a hybrid team — running stakeholder alignment, architecture decisions, and governance during US hours while an offshore team executes build and test. The Lead is accountable for turning the AI Hub roadmap from a backlog of capabilities into shipped, governed, production\-grade software delivered by an AI\-accelerated team.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
Our employees are tasked with delivering excellent business results through the efforts of their teams. These results are achieved by:
Enablement \& SDLC Transformation
- Own the AI Hub COE charter and act as the bridge between EVERSANA's Enterprise AI team and Patient Services engineering.
- Ingest and operationalize enterprise AI tooling (GCP, Vertex AI, Claude, Gemini Enterprise) into the day\-to\-day SDLC of the ACTICS (Salesforce Health Cloud), MuleSoft, and Java/.NET teams.
- Re\-architect existing engineering practice into an AI\-augmented model — standardizing AI\-assisted development with Claude Code, Cursor, and GitHub Copilot across Dev, QA, and BA functions.
- Define and drive the change\-management path so engineers adopt AI\-first workflows, not just have access to the tools.
Agent Architecture \& Deployment
- Architect the agent deployment and lifecycle framework on Vertex AI, with Claude and Gemini Enterprise as primary models.
- Establish reusable agent patterns — RAG pipelines, tool/function calling, MCP server integrations, multi\-step orchestration — that teams can build on.
- Set the standard for how agents are built, evaluated, deployed, monitored, and retired in production.
Governance \& Compliance
- Own AI governance for Patient Services: model selection criteria, PHI/HIPAA handling, evaluation frameworks, and the approved\-tools standard.
- Ensure every agent and AI workflow meets healthcare compliance requirements before production, coordinating with InfoSec on data\-flow approval and BAA verification.
- Maintain the AI risk register and the prompt/pattern library governance process.
Delivery Leadership
- Lead a hybrid onshore/offshore team on a follow\-the\-sun model — architecture and stakeholder alignment during US hours, offshore execution overnight, delivered to a ready queue each morning.
- Plan and run parallel\-track delivery so multiple AI MVPs and tech workstreams progress simultaneously against a compressed roadmap.
- Define AI velocity KPIs (code\-generation rate, defect\-rate delta, time\-to\-merge, story points per sprint) and report progress and ROI quarterly to the CTO and CFO.
Stakeholder Interface
- Serve as the senior technical voice for AI in Patient Services with the CTO, CFO, Enterprise AI leadership, and external vendor partners.
- Coordinate with adjacent pods (ACTICS, NiCE, MuleSoft integration) and existing product teams as their capacity flows into AI Hub work.
*Consistent with the Americans with Disabilities Act (ADA) and applicable state and local laws, it is the policy of EVERSANA to provide reasonable accommodation when requested by an employee with a disability, unless such accommodation would cause an undue hardship for EVERSANA. If reasonable accommodation is needed to perform the essential functions of your job position, please contact Human Resources.*
EXPECTATIONS OF THE JOB:
- Travel (Minimal)
- Hours (40 hours, Monday through Friday)
*The above list reflects the general details necessary to describe the expectations of the position and shall not be construed as the only expectations that may be assigned for the position.*
*An individual in this position must be able to successfully perform the expectations listed above*
Qualifications MINIMUM KNOWLEDGE, SKILLS AND ABILITIES:
- 8\+ years in software engineering, with 3\+ years in a technical lead or architect capacity.
- Demonstrated experience architecting and deploying LLM\-based systems or AI agents in production — not just prototypes.
- Hands\-on fluency with a major cloud AI platform (Vertex AI strongly preferred; AWS Bedrock or Azure OpenAI acceptable) and with leading LLMs (Claude, Gemini, or equivalent).
- Working knowledge of agent design patterns: RAG, tool use / function calling, orchestration frameworks (CrewAI, LangChain, or Vertex Agent Builder), and emerging standards such as MCP.
- Experience introducing AI\-assisted development tooling (Claude Code, GitHub Copilot, Cursor, or similar) into an engineering organization and driving adoption.
- Familiarity with the Salesforce ecosystem and enterprise integration (MuleSoft or comparable) sufficient to guide architecture decisions.
- Strong grasp of governance and compliance for AI in a regulated environment — HIPAA/PHI handling, model risk, and data security.
- Excellent executive communication; able to translate technical strategy into business terms for CTO/CFO audiences.
- Experience leading distributed onshore/offshore teams.
PREFERRED QUALIFICATIONS:
- Background in healthcare, life sciences, or pharmaceutical patient services technology.
- Prior experience standing up an AI Center of Excellence or similar enablement function.
- Salesforce Health Cloud, Apex, or LWC experience.
- Vendor\-management experience with AI or healthcare\-technology partners.
PHYSICAL/MENTAL DEMANDS AND WORKING ENVIRONMENT:
The physical and mental requirements along with the work environment characteristics described here are representative of those an individual encounters while performing the essential functions of this position.
Office: While performing the essential functions of this job the employee is frequently required to reach, grasp, stand and/or sit for long periods of time (up to 90% of the shift), walk, talk and hear; occasionally required to lift and/or move up to 25 pounds. The noise level in the work environment is usually moderately quiet, with frequent interruptions and multiple demands.
Additional Information OUR CULTURAL BELIEFS:
Patient Minded I act with the patient’s best interest in mind.
Client Delight I own every client experience and its impact on results.
Take Action I am empowered and hold myself accountable.
Embrace Diversity I create an environment of awareness and respect.
Grow Talent I own my development and invest in the development of others.
Win Together I passionately connect with anyone, anywhere, anytime to achieve results.
Communication Matters I speak up to create transparent, thoughtful, and timely dialogue.
Always Innovate I am bold and creative in everything I do.
EVERSANA is committed to providing competitive salaries and benefits for all employees. The anticipated base salary range for this position is $84,00 to $117,000 and is not applicable to locations outside of the U.S. The base salary range represents the low and high end of the salary range for this position. Compensation will be determined based on relevant experience, other job\-related qualifications/skills, and geographic location (to account for comparative cost of living). EVERSANA reserves the right to modify this base salary range at any time.
All your information will be kept confidential according to EEO guidelines.
Our team is aware of recent fraudulent job offers in the market, misrepresenting EVERSANA. Recruitment fraud is a sophisticated scam commonly perpetrated through online services using fake websites, unsolicited e\-mails, or even text messages claiming to be a legitimate company. Some of these scams request personal information and even payment for training or job application fees. Please know EVERSANA would never require personal information nor payment of any kind during the employment process. We respect the personal rights of all candidates looking to explore careers at EVERSANA.
EVERSANA is committed to providing competitive salaries and benefits for all employees. If this job posting includes a base salary range, it represents the low and high end of the salary range for this position and is not applicable to locations outside of the U.S. Compensation will be determined based on relevant experience, other job\-related qualifications/skills, and geographic location (to account for comparative cost of living). This role is eligible for hire in select U.S. locations based on business and operational considerations. For a full list of locations, visit eversana.com/careers. More information about EVERSANA’s benefits package can be found at eversana.com/careers. EVERSANA reserves the right to modify this base salary range and benefits at any time.
From EVERSANA’s inception, Diversity, Equity \& Inclusion have always been key to our success. We are an Equal Opportunity Employer, and our employees are people with different strengths, experiences, and backgrounds who share a passion for improving the lives of patients and leading innovation within the healthcare industry. Diversity not only includes race and gender identity, but also age, disability status, veteran status, sexual orientation, religion, and many other parts of one’s identity. All of our employees’ points of view are key to our success, and inclusion is everyone's responsibility.
Consistent with the Americans with Disabilities Act (ADA) and applicable state and local laws, it is the policy of EVERSANA to provide reasonable accommodation when requested by a qualified applicant or candidate with a disability, unless such accommodation would cause an undue hardship for EVERSANA. The policy regarding requests for reasonable accommodations applies to all aspects of the hiring process. If reasonable accommodation is needed to participate in the interview and hiring process, please contact us at [email protected].
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Salary Context
This $178K-$213K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At EVERSANA, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($195K) sits 9% below the category median. Disclosed range: $178K to $213K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
EVERSANA AI Hiring
EVERSANA has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Overland Park, KS, US, Chicago, IL, US. Compensation range: $207K - $213K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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